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Road Terrain Classification Technology for Autonomous Vehicle [electronic resource] / by Shifeng Wang.

By: Wang, Shifeng [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Unmanned System Technologies: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XVI, 97 p. 43 illus., 32 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811361555.Subject(s): Automotive engineering | Artificial intelligence | Transportation engineering | Traffic engineering | Signal processing | Automotive Engineering | Artificial Intelligence | Transportation Technology and Traffic Engineering | Signal, Speech and Image ProcessingAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 629.2 Online resources: Click here to access online
Contents:
Introduction -- Review of Related Work -- Acceleration Based Road Terrain Classification -- Image Based Road Terrain Classification -- LRF Based Road Terrain Classification -- Multiple-Sensor Based Road Terrain Classification -- Conclusion and Future Direction.
In: Springer Nature eBookSummary: This book provides cutting-edge insights into autonomous vehicles and road terrain classification, and introduces a more rational and practical method for identifying road terrain. It presents the MRF algorithm, which combines the various sensors’ classification results to improve the forward LRF for predicting upcoming road terrain types. The comparison between the predicting LRF and its corresponding MRF show that the MRF multiple-sensor fusion method is extremely robust and effective in terms of classifying road terrain. The book also demonstrates numerous applications of road terrain classification for various environments and types of autonomous vehicle, and includes abundant illustrations and models to make the comparison tables and figures more accessible. .
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Introduction -- Review of Related Work -- Acceleration Based Road Terrain Classification -- Image Based Road Terrain Classification -- LRF Based Road Terrain Classification -- Multiple-Sensor Based Road Terrain Classification -- Conclusion and Future Direction.

This book provides cutting-edge insights into autonomous vehicles and road terrain classification, and introduces a more rational and practical method for identifying road terrain. It presents the MRF algorithm, which combines the various sensors’ classification results to improve the forward LRF for predicting upcoming road terrain types. The comparison between the predicting LRF and its corresponding MRF show that the MRF multiple-sensor fusion method is extremely robust and effective in terms of classifying road terrain. The book also demonstrates numerous applications of road terrain classification for various environments and types of autonomous vehicle, and includes abundant illustrations and models to make the comparison tables and figures more accessible. .

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